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Article

Conditioning Machine Learning Models to Adjust Lowbush Blueberry Crop Management to the Local Agroecosystem

1
Department of Soils and Agri-Food Engineering, Université Laval, Québec QC G1V 0A6, Canada
2
Agriculture and Agri-Food Canada, 1468 Rue Saint-Cyrille, Normandin QC G8M 4K3, Canada
3
Département des Sciences Fondamentales, Université du Québec à Chicoutimi, 555, Boulevard de l’Université, Chicoutimi QC G7H 2B1, Canada
4
Departamento de Solos, Universidade Federal de Santa Maria, Camobi Santa Maria, Rio Grande do Sul 97105-900, Brazil
5
Agriculture and Agri-Food Canada, Quebec Research and Development Centre, 2560 Hochelaga Blvd., Québec QC G1V 2J3, Canada
*
Author to whom correspondence should be addressed.
Plants 2020, 9(10), 1401; https://doi.org/10.3390/plants9101401
Received: 17 September 2020 / Revised: 13 October 2020 / Accepted: 16 October 2020 / Published: 21 October 2020
Agroecosystem conditions limit the productivity of lowbush blueberry. Our objectives were to investigate the effects on berry yield of agroecosystem and crop management variables, then to develop a recommendation system to adjust nutrient and soil management of lowbush blueberry to given local meteorological conditions. We collected 1504 observations from N-P-K fertilizer trials conducted in Quebec, Canada. The data set, that comprised soil, tissue, and meteorological data, was processed by Bayesian mixed models, machine learning, compositional data analysis, and Markov chains. Our investigative statistical models showed that meteorological indices had the greatest impact on yield. High mean temperature at flower bud opening and after fruit maturation, and total precipitation at flowering stage showed positive effects. Low mean temperature and low total precipitation before bud opening, at flowering, and by fruit maturity, as well as number of freezing days (<−5 °C) before flower bud opening, showed negative effects. Soil and tissue tests, and N-P-K fertilization showed smaller effects. Gaussian processes predicted yields from historical weather data, soil test, fertilizer dosage, and tissue test with a root-mean-square-error of 1447 kg ha−1. An in-house Markov chain algorithm optimized yields modelled by Gaussian processes from tissue test, soil test, and fertilizer dosage as conditioned to specified historical meteorological features, potentially increasing yield by a median factor of 1.5. Machine learning, compositional data analysis, and Markov chains allowed customizing nutrient management of lowbush blueberry at local scale. View Full-Text
Keywords: blueberry; crop modeling; plant nutrition; machine learning blueberry; crop modeling; plant nutrition; machine learning
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MDPI and ACS Style

Parent, S.-É.; Lafond, J.; Paré, M.C.; Parent, L.E.; Ziadi, N. Conditioning Machine Learning Models to Adjust Lowbush Blueberry Crop Management to the Local Agroecosystem. Plants 2020, 9, 1401. https://doi.org/10.3390/plants9101401

AMA Style

Parent S-É, Lafond J, Paré MC, Parent LE, Ziadi N. Conditioning Machine Learning Models to Adjust Lowbush Blueberry Crop Management to the Local Agroecosystem. Plants. 2020; 9(10):1401. https://doi.org/10.3390/plants9101401

Chicago/Turabian Style

Parent, Serge-Étienne, Jean Lafond, Maxime C. Paré, Léon E. Parent, and Noura Ziadi. 2020. "Conditioning Machine Learning Models to Adjust Lowbush Blueberry Crop Management to the Local Agroecosystem" Plants 9, no. 10: 1401. https://doi.org/10.3390/plants9101401

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